Ensuring electronic medical record simulation through better training, modeling, and evaluation.

ABSTRACT:

Objective

Electronic medical records (EMRs) can support medical research and discovery, but privacy risks limit the sharing of such data on a wide scale. Various approaches have been developed to mitigate risk, including record simulation via generative adversarial networks (GANs). While showing promise in certain application domains, GANs lack a principled approach for EMR data that induces subpar simulation. In this article, we improve EMR simulation through a novel pipeline that (1) enhances the learning model, (2) incorporates evaluation criteria for data utility that informs learning, and (3) refines the training process.

Materials and methods

We propose a new electronic health record generator using a GAN with a Wasserstein divergence and layer normalization techniques

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC6913223 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature